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This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine.

Technology: general issues --- History of engineering & technology --- electrocardiogram --- deep metric learning --- k-nearest neighbors classifier --- premature ventricular contraction --- dimensionality reduction --- classifications --- Laplacian eigenmaps --- locality preserving projections --- compressed sensing --- convolutional neural network --- EEG --- epileptic seizure detection --- RISC-V --- ultra-low-power --- sepsis --- atrial fibrillation --- prediction --- heart rate variability --- feature extraction --- random forest --- annotations --- myoelectric prosthesis --- sEMG --- grasp phases analysis --- grasp classification --- machine learning --- electronic nose --- liver dysfunction --- cirrhosis --- semiconductor metal oxide gas sensor --- vagus nerve --- intraneural --- decoding --- intrafascicular --- recording --- carbon nanotube --- artificial intelligence --- lens-free shadow imaging technique --- cell-line analysis --- cell signal enhancement --- deep learning --- ECG signal --- reconstruction dictionaries --- projection matrices --- signal classifications --- osteopenia --- sarcopenia --- XAI --- SHAP --- IMU --- gait analysis --- sensors --- convolutional neural networks --- Parkinson's disease --- biomedical monitoring --- accelerometer --- pressure sensor --- disease management --- electromyography --- correlation --- high blood pressure --- hypertension --- photoplethysmography --- electrocardiography --- calibration --- classification models --- COVID-19 --- ECG trace image --- transfer learning --- Convolutional Neural Networks (CNN) --- feature selection --- sympathetic activity (SNA) --- skin sympathetic nerve activity (SKNA) --- electrodes --- electrocardiogram (ECG) --- cardiac time interval --- dynamic time warping --- fiducial point detection --- heart failure --- seismocardiography --- wearable electroencephalography --- motor imagery --- motor execution --- beta rebound --- brain-machine interface --- EEG classification --- electrocardiogram --- deep metric learning --- k-nearest neighbors classifier --- premature ventricular contraction --- dimensionality reduction --- classifications --- Laplacian eigenmaps --- locality preserving projections --- compressed sensing --- convolutional neural network --- EEG --- epileptic seizure detection --- RISC-V --- ultra-low-power --- sepsis --- atrial fibrillation --- prediction --- heart rate variability --- feature extraction --- random forest --- annotations --- myoelectric prosthesis --- sEMG --- grasp phases analysis --- grasp classification --- machine learning --- electronic nose --- liver dysfunction --- cirrhosis --- semiconductor metal oxide gas sensor --- vagus nerve --- intraneural --- decoding --- intrafascicular --- recording --- carbon nanotube --- artificial intelligence --- lens-free shadow imaging technique --- cell-line analysis --- cell signal enhancement --- deep learning --- ECG signal --- reconstruction dictionaries --- projection matrices --- signal classifications --- osteopenia --- sarcopenia --- XAI --- SHAP --- IMU --- gait analysis --- sensors --- convolutional neural networks --- Parkinson's disease --- biomedical monitoring --- accelerometer --- pressure sensor --- disease management --- electromyography --- correlation --- high blood pressure --- hypertension --- photoplethysmography --- electrocardiography --- calibration --- classification models --- COVID-19 --- ECG trace image --- transfer learning --- Convolutional Neural Networks (CNN) --- feature selection --- sympathetic activity (SNA) --- skin sympathetic nerve activity (SKNA) --- electrodes --- electrocardiogram (ECG) --- cardiac time interval --- dynamic time warping --- fiducial point detection --- heart failure --- seismocardiography --- wearable electroencephalography --- motor imagery --- motor execution --- beta rebound --- brain-machine interface --- EEG classification

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• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine.

Technology: general issues --- History of engineering & technology --- electrocardiogram --- deep metric learning --- k-nearest neighbors classifier --- premature ventricular contraction --- dimensionality reduction --- classifications --- Laplacian eigenmaps --- locality preserving projections --- compressed sensing --- convolutional neural network --- EEG --- epileptic seizure detection --- RISC-V --- ultra-low-power --- sepsis --- atrial fibrillation --- prediction --- heart rate variability --- feature extraction --- random forest --- annotations --- myoelectric prosthesis --- sEMG --- grasp phases analysis --- grasp classification --- machine learning --- electronic nose --- liver dysfunction --- cirrhosis --- semiconductor metal oxide gas sensor --- vagus nerve --- intraneural --- decoding --- intrafascicular --- recording --- carbon nanotube --- artificial intelligence --- lens-free shadow imaging technique --- cell-line analysis --- cell signal enhancement --- deep learning --- ECG signal --- reconstruction dictionaries --- projection matrices --- signal classifications --- osteopenia --- sarcopenia --- XAI --- SHAP --- IMU --- gait analysis --- sensors --- convolutional neural networks --- Parkinson’s disease --- biomedical monitoring --- accelerometer --- pressure sensor --- disease management --- electromyography --- correlation --- high blood pressure --- hypertension --- photoplethysmography --- electrocardiography --- calibration --- classification models --- COVID-19 --- ECG trace image --- transfer learning --- Convolutional Neural Networks (CNN) --- feature selection --- sympathetic activity (SNA) --- skin sympathetic nerve activity (SKNA) --- electrodes --- electrocardiogram (ECG) --- cardiac time interval --- dynamic time warping --- fiducial point detection --- heart failure --- seismocardiography --- wearable electroencephalography --- motor imagery --- motor execution --- beta rebound --- brain–machine interface --- EEG classification --- n/a --- Parkinson's disease --- brain-machine interface

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine.

electrocardiogram --- deep metric learning --- k-nearest neighbors classifier --- premature ventricular contraction --- dimensionality reduction --- classifications --- Laplacian eigenmaps --- locality preserving projections --- compressed sensing --- convolutional neural network --- EEG --- epileptic seizure detection --- RISC-V --- ultra-low-power --- sepsis --- atrial fibrillation --- prediction --- heart rate variability --- feature extraction --- random forest --- annotations --- myoelectric prosthesis --- sEMG --- grasp phases analysis --- grasp classification --- machine learning --- electronic nose --- liver dysfunction --- cirrhosis --- semiconductor metal oxide gas sensor --- vagus nerve --- intraneural --- decoding --- intrafascicular --- recording --- carbon nanotube --- artificial intelligence --- lens-free shadow imaging technique --- cell-line analysis --- cell signal enhancement --- deep learning --- ECG signal --- reconstruction dictionaries --- projection matrices --- signal classifications --- osteopenia --- sarcopenia --- XAI --- SHAP --- IMU --- gait analysis --- sensors --- convolutional neural networks --- Parkinson’s disease --- biomedical monitoring --- accelerometer --- pressure sensor --- disease management --- electromyography --- correlation --- high blood pressure --- hypertension --- photoplethysmography --- electrocardiography --- calibration --- classification models --- COVID-19 --- ECG trace image --- transfer learning --- Convolutional Neural Networks (CNN) --- feature selection --- sympathetic activity (SNA) --- skin sympathetic nerve activity (SKNA) --- electrodes --- electrocardiogram (ECG) --- cardiac time interval --- dynamic time warping --- fiducial point detection --- heart failure --- seismocardiography --- wearable electroencephalography --- motor imagery --- motor execution --- beta rebound --- brain–machine interface --- EEG classification --- n/a --- Parkinson's disease --- brain-machine interface

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This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains.

tourism big data --- text mining --- NLP --- deep learning --- clinical named entity recognition --- information extraction --- multitask model --- long short-term memory --- conditional random field --- relation extraction --- entity recognition --- long short-term memory network --- multi-turn chatbot --- dialogue context encoding --- WGAN-based response generation --- BERT word embedding --- text summary --- reinforce learning --- FAQ classification --- encoder-decoder neural network --- multi-level word embeddings --- BERT --- bidirectional RNN --- cloze test --- Korean dataset --- machine comprehension --- neural language model --- sentence completion --- primary healthcare --- chief complaint --- virtual medical assistant --- spoken natural language --- disease diagnosis --- medical specialist --- protein–protein interactions --- deep learning (DL) --- convolutional neural networks (CNN) --- bidirectional long short-term memory (bidirectional LSTM) --- dialogue management --- user simulation --- reward shaping --- conversation knowledge --- multi-agent reinforcement learning --- language modeling --- classification --- error probability --- error assessment --- logic error --- neural network --- LSTM --- attention mechanism --- programming education --- neural architecture search --- word ordering --- Korean syntax --- adversarial attack --- adversarial example --- sentiment classification --- dual pointer network --- context-to-entity attention --- text classification --- rule-based --- word embedding --- Doc2vec --- paraphrase identification --- encodings --- R-GCNs --- contextual features --- sentence retrieval --- TF−ISF --- BM25 --- partial match --- sequence similarity --- word to vector --- word embeddings --- antonymy detection --- polarity --- text normalization --- natural language processing --- deep neural networks --- causal encoder --- question classification --- multilingual --- convolutional neural networks --- Natural Language Processing (NLP) --- transfer learning --- open information extraction --- recurrent neural networks --- bilingual translation --- speech-to-text --- LaTeX decompilation --- word representation learning --- word2vec --- sememes --- structural information --- sentiment analysis --- zero-shot learning --- news analysis --- cross-lingual classification --- multilingual transformers --- knowledge base --- commonsense --- sememe prediction --- attention model --- ontologies --- fixing ontologies --- quick fix --- quality metrics --- online social networks --- rumor detection --- Cantonese --- XGA model --- delayed combination --- CNN dictionary --- named entity recognition --- deep learning NER --- bidirectional LSTM CRF --- CoNLL --- OntoNotes --- toxic comments --- neural networks --- n/a --- protein-protein interactions